About Azure Machine Learning

Accelerate the end-to-end machine learning lifecycle

Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible AI.

Start quickly with an optimized Apache Spark environment

Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

Boost productivity and access ML for all skills

Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level. Use the no-code designer to get started, or use built-in Jupyter notebooks for a code-first experience. Accelerate model creation with the automated machine learning UI, and access built-in feature engineering, algorithm selection, and hyperparameter sweeping to develop highly accurate models.

Operationalize at scale with robust MLOps

MLOps, or DevOps for machine learning, streamlines the machine learning lifecycle, from building models to deployment and management. Use ML pipelines to build repeatable workflows, and use a rich model registry to track your assets. Manage production workflows at scale using advanced alerts and machine learning automation capabilities. Profile, validate, and deploy machine learning models anywhere, from the cloud to the edge, to manage production ML workflows at scale in an enterprise-ready fashion.

Build responsible AI solutions

Access state-of-the-art technology for fairness and machine learning model transparency. Use model interpretability for explanations about predictions to better understand model behavior. Reduce model bias by applying common fairness metrics, automatically making comparisons and using recommended mitigations.